A three-dimensional surficial geology model of southern Ontario: progress report
Bibliographic record
Abstract
A 3-D model consisting of 7 surficial geology layers overlying bedrock for 66,870 km2 of southern Ontario has been constructed. Model development involved the assembly of a comprehensive subsurface database that includes archival water well and geotechnical material logs, cored boreholes, and stratigraphically interpreted geophysical data. Other geospatial constraints are derived from topographic Digital Elevation Models (DEM), bathymetric DEMs and depth soundings, a bedrock surface DEM, and seamless surficial geology mapping. Existing sub-regional (<10,000 km2) higher resolution 3-D models were used to support the model development. The surficial geological legend provided the basis for a simplified stratigraphic layer structure (from oldest to youngest): 1-Bedrock, 2-Lower Sediment, 3-Regional Till, 4-Glaciofluvial Sediment, 5-Upper Till, 6-Glaciolacustrine Mud, 7-Glaciolacustrine Sand, and 8-Recent/Organic Sediment. A preliminary model based on high-quality interpreted data was used along with surficial geological mapping and expert knowledge in a rules-based algorithm to help assign stratigraphic coding to the widespread, archival material log data. An iterative cycle of automated coding, manual coding, periodic interim model inspection and revision has led to this 3-D surficial geological model. This model supports regional-scale groundwater flow modelling and, along with a companion model of bedrock geology, will comprise the first complete 3-D model coverage of southern Ontario from the Precambrian basement to post-glacial sediment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".